Dr. Purang Abolmaesumi
Professor
Electrical and Computer Engineering
Purang Abolmaesumi received his BSc (1995) and MSc (1997) from Sharif University of Technology, Iran, and his PhD (2002) from UBC, all in electrical engineering. From 2002 to 2009, he was a faculty member with the School of Computing, Queen’s University. He then joined the Department of Electrical and Computer Engineering at UBC, where he is a Canada Research Chair, Tier II, in Biomedical Engineering and a Professor, with Associate Membership to the Department of Urologic Sciences. He is also the recipient of the Killam Faculty Research Prize and the Killam Award for Excellence in Mentoring at UBC.
Research Focus
Dr. Abolmaesumi is internationally recognized and has received numerous awards for his pioneering developments in medical image analysis and image-guided interventions. Dr. Abolmaesumi carries out research in medical imaging, machine learning, and image-guided diagnosis and interventions. Artificial intelligence and machine learning techniques are applied to diagnosis in ultrasound, magnetic resonance imaging, digitized pathology slides and other tissue images.
Example Project
“Multi-Centre Temporal Ultrasound Image Analysis for Prostate Cancer Diagnosis”
Prostate cancer (PCa) is the most common cancer diagnosed in Canadian men. The gold standard for PCa diagnosis and prognosis is ultrasound (US)-guided needle biopsy with pathologic grading. The best biopsy technique today is to localize PCa with magnetic resonance imaging (MRI), and use its fusion with US to improve the detection rate of aggressive PCa. However, this method alone is not able to accurately differentiate indolent PCa and cannot reliably detect small lesions of aggressive PCa. In this project, we propose a turnkey technology, where subtle variations of backscattered US signals from a tissue location over a short time span, i.e., temporal enhanced US, are used to detect physical properties of the underlying tissue using a machine-learning framework. Our objective is to establish the efficacy of this technology in identifying clinically significant PCa in real-time US, so that accurate biopsy targeting is achieved. In a series of clinical studies, we will develop and deploy this technology across multiple institutions. This technology is expected to positively and rapidly benefit the lives of all patients suffering from PCa.
Research Keywords
Biomedical Engineering, Machine Learning, Medical Image Analysis, Computer-Assisted Surgery
